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dc.contributor.authorZheng, Shuangjia
dc.contributor.authorLei, Zengrong
dc.contributor.authorAi, Haitao
dc.contributor.authorChen, Hongming
dc.contributor.authorDeng, Daiguo
dc.contributor.authorYang, Yuedong
dc.date.accessioned2022-02-04T03:15:21Z
dc.date.available2022-02-04T03:15:21Z
dc.date.issued2021
dc.identifier.issn1758-2946
dc.identifier.doi10.1186/s13321-021-00565-5
dc.identifier.urihttp://hdl.handle.net/10072/411991
dc.description.abstractScaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherBMC
dc.relation.ispartofpagefrom87
dc.relation.ispartofissue1
dc.relation.ispartofjournalJournal of Cheminformatics
dc.relation.ispartofvolume13
dc.subject.fieldofresearchMacromolecular and materials chemistry
dc.subject.fieldofresearchcode3403
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsChemistry, Multidisciplinary
dc.subject.keywordsComputer Science, Information Systems
dc.titleDeep scaffold hopping with multimodal transformer neural networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationZheng, S; Lei, Z; Ai, H; Chen, H; Deng, D; Yang, Y, Deep scaffold hopping with multimodal transformer neural networks, Journal of Cheminformatics, 2021, 13 (1), pp. 87
dcterms.dateAccepted2021-10-31
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2022-02-04T02:58:14Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorYang, Yuedong


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